ヘルスケア不正検知市場：タイプ別（記述的分析、予測的分析、処方的分析）、コンポーネント別（サービス、ソフトウェア）、アプリケーション別（保険請求レビュー、支払いインテグリティ）、エンドユーザー別（ヘルスケア支払者、政府機関、その他）。世界の機会分析および産業予測、2021-2031年

Healthcare Fraud Detection Market By Type (Descriptive Analytics, Predictive Analytics, Prescriptive Analysis), By Component (Services, Software), By Application (Insurance Claims Review, Payment Integrity), By End User (Healthcare Payer, Government Agencies, Others): Global Opportunity Analysis and Industry Forecast, 2021-2031

医療用不正検知の世界市場は、2021年に10億9820万ドルとなり、2022年から2031年にかけて年平均成長率12.6%を記録し、2031年には36億ドルに達すると予測されます。不正検知の目的は、不正な手段で金銭などを入手... もっと見る

出版社	出版年月	電子版価格	ページ数	言語
Allied Market Research アライドマーケットリサーチ	2022年7月31日	US$5,820 シングルユーザライセンスライセンス・価格情報・注文方法はこちら	310	英語

日本語のページは自動翻訳を利用し作成しています。
実際のレポートは英文のみでご納品いたします。

サマリー

医療用不正検知の世界市場は、2021年に10億9820万ドルとなり、2022年から2031年にかけて年平均成長率12.6%を記録し、2031年には36億ドルに達すると予測されます。
不正検知の目的は、不正な手段で金銭などを入手することを阻止することです。医療やヘルスケアなど、さまざまな業界で不正検知の技術が使われている。医療の不正検知には、口座監査と探偵業務が含まれる。徹底的な口座監査により、疑わしい契約者やサプライヤーを発見することができるかもしれません。請求を一つずつ丁寧に監査するのが理想的です。しかし、すべての請求を監査する現実的な方法はありません。不正検出管理は、何百万もの取引に目を通し、パターンを見つけ不正を特定するためにデータを分類、整理、区分する技術、データマイニング、独立変数と従属変数の間の接続の推定などの技術によって行われます。データマッチングは、2つのデータコレクションを比較し、重複を排除し、データ間の接続を確立するために使用される技術である。

医療費不正、無駄、乱用は、実は医療費不正検知産業によって防がれています。医療詐欺とは、患者や医療従事者が意図的に事実を歪曲し、違法な支払いや利益を得ることです。医療不正の例としては、同じ患者に対して異なる医療機関が多数の請求を行うこと、医師によるデータの改ざん、提供されていないサービスの請求書の提出、各種治療の日付、回数、期間、サービス内容の虚偽の記載などが挙げられます。医療業界では、不正に関わるさまざまな行為が増加しています。さらに、不正事例の増加、医療製品や医療機器の不正使用、医療資金の不正使用は、市場の成長を促進すると予測されます。

医療機関における不正検知の市場成長を促進する主な要因は、医療保険を求める患者の増加です。その他、不正事例の増加や医療が提供する資金の不正使用などが、医療費不正支出検出市場の成長を後押ししています。医療保険の不正を手作業で発見するためには、少数の監査員が怪しい医療保険請求を手作業で評価し、ピンポイントで特定する必要があります。

しかし、近年の機械学習やデータマイニング技術の飛躍的進歩により、より効果的で自動化された医療費不正の検出方法が開発されてきています。近年、医療データのマイニングによる不正検出への関心が高まっており、世界の医療不正検出市場を後押ししています。機械学習と人工知能の画期的な進歩により、ヘルスケア産業におけるデータセキュリティの懸念が高まり、世界のヘルスケア不正検知市場を抑制しています。
ヘルスケア不正検知市場は、タイプ、コンポーネント、アプリケーション、エンドユーザー、地域に基づいてセグメント化されています。タイプ別では、市場は記述的分析、予測的分析、処方的分析に区分されます。コンポーネント別では、市場はサービスとソフトウェアに細分化されます。アプリケーション別では、保険金請求レビューと支払整合性に分けられます。

エンドユーザー別では、医療費支払機関、政府機関、その他に区分されます。医療費支払者は、さらに公的支払者と私的支払者に区分される。その他には、雇用者、医療提供者、第三者サービス提供者が含まれます。地域別では、北米、欧州、アジア太平洋地域、LAMEAで分析されています。
世界の医療不正検知市場で活動する主な主要企業は、International Business Machines Corporation (IBM), Optum, Verscend Technologies, McKesson Corporation, FAIR ISAAC Corporation, SAS Institute Inc, HCL Technologies, Wipro Limited, Conduent, CGI Group, DXC Technology Company, UnitedHealth Group, Exlservice Holdings Inc, Scio inspire Corp, LexisNexis, OSP Labs, Northrop Grumman.などが挙げられます。

ステークホルダーにとっての主なメリット
本レポートは、2021年から2031年までの医療不正検知市場分析の市場セグメント、現在のトレンド、予測、ダイナミクスを定量的に分析し、医療不正検知市場の優勢な機会を特定します。
●市場調査は、主要な推進要因、阻害要因、機会に関する情報とともに提供されます。
医療不正検知市場に関する2021年～2031年の分析結果をご紹介します。
医療不正検知市場のセグメンテーションの詳細な分析により、市場機会を決定します。
医療不正検知市場のセグメンテーションを詳細に分析することで、市場機会を特定することができます ●世界市場に対する収益貢献度に応じて、各地域の主要国をマッピングしています。
市場プレイヤーのポジショニングは、ベンチマークを容易にし、市場プレイヤーの現在のポジションを明確に理解することができます。
地域別及び世界の医療不正検知市場の動向、キープレイヤー、市場セグメント、アプリケーション分野、市場成長戦略などの分析が含まれています。

主な市場セグメント
アプリケーション別
保険金請求レビュー
支払いインテグリティ
エンドユーザー別
ヘルスケアペイヤー
タイプ別
公的支払者
民間事業者
政府機関
その他
タイプ別
記述的分析（Descriptive Analytics
予測分析（Predictive Analytics
プリスクリプティブ・アナリティクス
コンポーネント別
サービス
ソフトウェア

地域別
北米
米国
カナダ
メキシコ
欧州
ドイツ
フランス
イギリス
イタリア
スペイン
その他のヨーロッパ
アジア・パシフィック
中国
オーストラリア
インド
韓国
その他のアジア太平洋地域
日本
LAMEA
ブラジル
サウジアラビア
南アフリカ
LAMEAの他の地域

主な市場参加者
インターナショナル・ビジネス・マシンズ・コーポレーション(IBM)
オプティム社
バーセンド・テクノロジー
マッケソンコーポレーション
FAIR ISAAC株式会社
SAS Institute Inc.
HCLテクノロジーズ
ウィプロ・リミテッド
コンデュエント
CGIグループ
DXCテクノロジー社
ユナイテッドヘルス・グループ
Exlservice Holdings Inc.
コティヴィティ
レクシスネクシス
OSPラボ
ノースロップグラマン
ノースロップ・グラマン

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CHAPTER 1:INTRODUCTION
1.1.Report description
1.2.Key market segments
1.3.Key benefits to the stakeholders
1.4.Research Methodology
1.4.1.Secondary research
1.4.2.Primary research
1.4.3.Analyst tools and models
CHAPTER 2:EXECUTIVE SUMMARY
2.1.Key findings of the study
2.2.CXO Perspective
CHAPTER 3:MARKET OVERVIEW
3.1.Market definition and scope
3.2.Key findings
3.2.1.Top investment pockets
3.3.Porter’s five forces analysis
3.4.Top player positioning
3.5.Market dynamics
3.5.1.Drivers
3.5.2.Restraints
3.5.3.Opportunities
3.6.COVID-19 Impact Analysis on the market
CHAPTER 4: HEALTHCARE FRAUD DETECTION MARKET, BY TYPE
4.1 Overview
4.1.1 Market size and forecast
4.2 Descriptive Analytics
4.2.1 Key market trends, growth factors and opportunities
4.2.2 Market size and forecast, by region
4.2.3 Market analysis by country
4.3 Predictive Analytics
4.3.1 Key market trends, growth factors and opportunities
4.3.2 Market size and forecast, by region
4.3.3 Market analysis by country
4.4 Prescriptive Analysis
4.4.1 Key market trends, growth factors and opportunities
4.4.2 Market size and forecast, by region
4.4.3 Market analysis by country
CHAPTER 5: HEALTHCARE FRAUD DETECTION MARKET, BY COMPONENT
5.1 Overview
5.1.1 Market size and forecast
5.2 Services
5.2.1 Key market trends, growth factors and opportunities
5.2.2 Market size and forecast, by region
5.2.3 Market analysis by country
5.3 Software
5.3.1 Key market trends, growth factors and opportunities
5.3.2 Market size and forecast, by region
5.3.3 Market analysis by country
CHAPTER 6: HEALTHCARE FRAUD DETECTION MARKET, BY APPLICATION
6.1 Overview
6.1.1 Market size and forecast
6.2 Insurance Claims Review
6.2.1 Key market trends, growth factors and opportunities
6.2.2 Market size and forecast, by region
6.2.3 Market analysis by country
6.3 Payment Integrity
6.3.1 Key market trends, growth factors and opportunities
6.3.2 Market size and forecast, by region
6.3.3 Market analysis by country
CHAPTER 7: HEALTHCARE FRAUD DETECTION MARKET, BY END USER
7.1 Overview
7.1.1 Market size and forecast
7.2 Healthcare Payer
7.2.1 Key market trends, growth factors and opportunities
7.2.2 Market size and forecast, by region
7.2.3 Market analysis by country
7.2.4 Healthcare Payer Healthcare Fraud Detection Market by Type
7.2.4.1 Public Payers Market size and forecast, by region
7.2.4.2 Private players Market size and forecast, by region
7.3 Government Agencies
7.3.1 Key market trends, growth factors and opportunities
7.3.2 Market size and forecast, by region
7.3.3 Market analysis by country
7.4 Others
7.4.1 Key market trends, growth factors and opportunities
7.4.2 Market size and forecast, by region
7.4.3 Market analysis by country
CHAPTER 8: HEALTHCARE FRAUD DETECTION MARKET, BY REGION
8.1 Overview
8.1.1 Market size and forecast
8.2 North America
8.2.1 Key trends and opportunities
8.2.2 North America Market size and forecast, by Type
8.2.3 North America Market size and forecast, by Component
8.2.4 North America Market size and forecast, by Application
8.2.5 North America Market size and forecast, by End User
8.2.5.1 North America Healthcare Payer Healthcare Fraud Detection Market by Type
8.2.6 North America Market size and forecast, by country
8.2.6.1 U.S.
8.2.6.1.1 Market size and forecast, by Type
8.2.6.1.2 Market size and forecast, by Component
8.2.6.1.3 Market size and forecast, by Application
8.2.6.1.4 Market size and forecast, by End User
8.2.6.2 Canada
8.2.6.2.1 Market size and forecast, by Type
8.2.6.2.2 Market size and forecast, by Component
8.2.6.2.3 Market size and forecast, by Application
8.2.6.2.4 Market size and forecast, by End User
8.2.6.3 Mexico
8.2.6.3.1 Market size and forecast, by Type
8.2.6.3.2 Market size and forecast, by Component
8.2.6.3.3 Market size and forecast, by Application
8.2.6.3.4 Market size and forecast, by End User
8.3 Europe
8.3.1 Key trends and opportunities
8.3.2 Europe Market size and forecast, by Type
8.3.3 Europe Market size and forecast, by Component
8.3.4 Europe Market size and forecast, by Application
8.3.5 Europe Market size and forecast, by End User
8.3.5.1 Europe Healthcare Payer Healthcare Fraud Detection Market by Type
8.3.6 Europe Market size and forecast, by country
8.3.6.1 Germany
8.3.6.1.1 Market size and forecast, by Type
8.3.6.1.2 Market size and forecast, by Component
8.3.6.1.3 Market size and forecast, by Application
8.3.6.1.4 Market size and forecast, by End User
8.3.6.2 France
8.3.6.2.1 Market size and forecast, by Type
8.3.6.2.2 Market size and forecast, by Component
8.3.6.2.3 Market size and forecast, by Application
8.3.6.2.4 Market size and forecast, by End User
8.3.6.3 UK
8.3.6.3.1 Market size and forecast, by Type
8.3.6.3.2 Market size and forecast, by Component
8.3.6.3.3 Market size and forecast, by Application
8.3.6.3.4 Market size and forecast, by End User
8.3.6.4 Italy
8.3.6.4.1 Market size and forecast, by Type
8.3.6.4.2 Market size and forecast, by Component
8.3.6.4.3 Market size and forecast, by Application
8.3.6.4.4 Market size and forecast, by End User
8.3.6.5 Spain
8.3.6.5.1 Market size and forecast, by Type
8.3.6.5.2 Market size and forecast, by Component
8.3.6.5.3 Market size and forecast, by Application
8.3.6.5.4 Market size and forecast, by End User
8.3.6.6 Rest of Europe
8.3.6.6.1 Market size and forecast, by Type
8.3.6.6.2 Market size and forecast, by Component
8.3.6.6.3 Market size and forecast, by Application
8.3.6.6.4 Market size and forecast, by End User
8.4 Asia-Pacific
8.4.1 Key trends and opportunities
8.4.2 Asia-Pacific Market size and forecast, by Type
8.4.3 Asia-Pacific Market size and forecast, by Component
8.4.4 Asia-Pacific Market size and forecast, by Application
8.4.5 Asia-Pacific Market size and forecast, by End User
8.4.5.1 Asia-Pacific Healthcare Payer Healthcare Fraud Detection Market by Type
8.4.6 Asia-Pacific Market size and forecast, by country
8.4.6.1 Japan
8.4.6.1.1 Market size and forecast, by Type
8.4.6.1.2 Market size and forecast, by Component
8.4.6.1.3 Market size and forecast, by Application
8.4.6.1.4 Market size and forecast, by End User
8.4.6.2 China
8.4.6.2.1 Market size and forecast, by Type
8.4.6.2.2 Market size and forecast, by Component
8.4.6.2.3 Market size and forecast, by Application
8.4.6.2.4 Market size and forecast, by End User
8.4.6.3 Australia
8.4.6.3.1 Market size and forecast, by Type
8.4.6.3.2 Market size and forecast, by Component
8.4.6.3.3 Market size and forecast, by Application
8.4.6.3.4 Market size and forecast, by End User
8.4.6.4 India
8.4.6.4.1 Market size and forecast, by Type
8.4.6.4.2 Market size and forecast, by Component
8.4.6.4.3 Market size and forecast, by Application
8.4.6.4.4 Market size and forecast, by End User
8.4.6.5 South Korea
8.4.6.5.1 Market size and forecast, by Type
8.4.6.5.2 Market size and forecast, by Component
8.4.6.5.3 Market size and forecast, by Application
8.4.6.5.4 Market size and forecast, by End User
8.4.6.6 Rest of Asia-Pacific
8.4.6.6.1 Market size and forecast, by Type
8.4.6.6.2 Market size and forecast, by Component
8.4.6.6.3 Market size and forecast, by Application
8.4.6.6.4 Market size and forecast, by End User
8.5 LAMEA
8.5.1 Key trends and opportunities
8.5.2 LAMEA Market size and forecast, by Type
8.5.3 LAMEA Market size and forecast, by Component
8.5.4 LAMEA Market size and forecast, by Application
8.5.5 LAMEA Market size and forecast, by End User
8.5.5.1 LAMEA Healthcare Payer Healthcare Fraud Detection Market by Type
8.5.6 LAMEA Market size and forecast, by country
8.5.6.1 Brazil
8.5.6.1.1 Market size and forecast, by Type
8.5.6.1.2 Market size and forecast, by Component
8.5.6.1.3 Market size and forecast, by Application
8.5.6.1.4 Market size and forecast, by End User
8.5.6.2 Saudi Arabia
8.5.6.2.1 Market size and forecast, by Type
8.5.6.2.2 Market size and forecast, by Component
8.5.6.2.3 Market size and forecast, by Application
8.5.6.2.4 Market size and forecast, by End User
8.5.6.3 South Africa
8.5.6.3.1 Market size and forecast, by Type
8.5.6.3.2 Market size and forecast, by Component
8.5.6.3.3 Market size and forecast, by Application
8.5.6.3.4 Market size and forecast, by End User
8.5.6.4 Rest of LAMEA
8.5.6.4.1 Market size and forecast, by Type
8.5.6.4.2 Market size and forecast, by Component
8.5.6.4.3 Market size and forecast, by Application
8.5.6.4.4 Market size and forecast, by End User
CHAPTER 9: COMPANY LANDSCAPE
9.1. Introduction
9.2. Top winning strategies
9.3. Product Mapping of Top 10 Player
9.4. Competitive Dashboard
9.5. Competitive Heatmap
9.6. Key developments
CHAPTER 10: COMPANY PROFILES
10.1 International Business Machines Corporation (IBM)
10.1.1 Company overview
10.1.2 Company snapshot
10.1.3 Operating business segments
10.1.4 Product portfolio
10.1.5 Business performance
10.1.6 Key strategic moves and developments
10.2 Optum Inc.
10.2.1 Company overview
10.2.2 Company snapshot
10.2.3 Operating business segments
10.2.4 Product portfolio
10.2.5 Business performance
10.2.6 Key strategic moves and developments
10.3 Verscend Technologies
10.3.1 Company overview
10.3.2 Company snapshot
10.3.3 Operating business segments
10.3.4 Product portfolio
10.3.5 Business performance
10.3.6 Key strategic moves and developments
10.4 McKesson Corporation
10.4.1 Company overview
10.4.2 Company snapshot
10.4.3 Operating business segments
10.4.4 Product portfolio
10.4.5 Business performance
10.4.6 Key strategic moves and developments
10.5 FAIR ISAAC Corporation
10.5.1 Company overview
10.5.2 Company snapshot
10.5.3 Operating business segments
10.5.4 Product portfolio
10.5.5 Business performance
10.5.6 Key strategic moves and developments
10.6 SAS Institute Inc.
10.6.1 Company overview
10.6.2 Company snapshot
10.6.3 Operating business segments
10.6.4 Product portfolio
10.6.5 Business performance
10.6.6 Key strategic moves and developments
10.7 HCL Technologies
10.7.1 Company overview
10.7.2 Company snapshot
10.7.3 Operating business segments
10.7.4 Product portfolio
10.7.5 Business performance
10.7.6 Key strategic moves and developments
10.8 WIPRO LIMITED
10.8.1 Company overview
10.8.2 Company snapshot
10.8.3 Operating business segments
10.8.4 Product portfolio
10.8.5 Business performance
10.8.6 Key strategic moves and developments
10.9 Conduent
10.9.1 Company overview
10.9.2 Company snapshot
10.9.3 Operating business segments
10.9.4 Product portfolio
10.9.5 Business performance
10.9.6 Key strategic moves and developments
10.10 CGI Group
10.10.1 Company overview
10.10.2 Company snapshot
10.10.3 Operating business segments
10.10.4 Product portfolio
10.10.5 Business performance
10.10.6 Key strategic moves and developments
10.11 DXC Technology Company
10.11.1 Company overview
10.11.2 Company snapshot
10.11.3 Operating business segments
10.11.4 Product portfolio
10.11.5 Business performance
10.11.6 Key strategic moves and developments
10.12 UnitedHealth Group, Inc.
10.12.1 Company overview
10.12.2 Company snapshot
10.12.3 Operating business segments
10.12.4 Product portfolio
10.12.5 Business performance
10.12.6 Key strategic moves and developments
10.13 Exlservice Holdings Inc.
10.13.1 Company overview
10.13.2 Company snapshot
10.13.3 Operating business segments
10.13.4 Product portfolio
10.13.5 Business performance
10.13.6 Key strategic moves and developments
10.14 Cotiviti Inc.
10.14.1 Company overview
10.14.2 Company snapshot
10.14.3 Operating business segments
10.14.4 Product portfolio
10.14.5 Business performance
10.14.6 Key strategic moves and developments
10.15 LexisNexis
10.15.1 Company overview
10.15.2 Company snapshot
10.15.3 Operating business segments
10.15.4 Product portfolio
10.15.5 Business performance
10.15.6 Key strategic moves and developments
10.16 OSP Labs
10.16.1 Company overview
10.16.2 Company snapshot
10.16.3 Operating business segments
10.16.4 Product portfolio
10.16.5 Business performance
10.16.6 Key strategic moves and developments
10.17 Northrop Grumman
10.17.1 Company overview
10.17.2 Company snapshot
10.17.3 Operating business segments
10.17.4 Product portfolio
10.17.5 Business performance
10.17.6 Key strategic moves and developments
10.18 Northrop Grumman Corp
10.18.1 Company overview
10.18.2 Company snapshot
10.18.3 Operating business segments
10.18.4 Product portfolio
10.18.5 Business performance
10.18.6 Key strategic moves and developments

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Table of Contents

Summary

The global healthcare fraud detection Market was valued at $1,098.2 million in 2021, and is projected to reach $3,600.0 million by 2031, registering a CAGR of 12.6% from 2022 to 2031.
The goal of fraud detection is to stop someone from obtaining money or other items through deceptive means. Various industries, including medical and healthcare, use fraud detection techniques. healthcare fraud detection involves account audits and detective work. A thorough account audit might discover suspect policy holders and suppliers. It is ideal to carefully audit each and every claim one at a time. However, there are no realistic way to audit all claims. Fraud detection management is done by the techniques such as to look through millions of transactions, classify, organize, and segment data in order to locate patterns and identify fraud, data mining, estimation of the connections between independent and dependent variables. Data matching is a technique used to compare two collections of data, remove out duplicates, and establish connections between data.

Healthcare fraud, waste, and abuse are actually prevented by the healthcare fraud detection industry. Healthcare fraud is the deliberate distortion of facts by patients or healthcare personnel that results in unlawful payments or benefits. Examples of healthcare fraud include the filing of numerous claims by different providers for the same patients, the falsification of data by doctors, the submission of claims for services that have not been rendered, and the misrepresentation of dates for various treatments, frequency, duration, or service descriptions. The various activities involving fraud in medical industries has increased. Furthermore, the increased fraud cases, abuse of medical products and equipment and misuse of healthcare funds is projected to drive the market growth.

The major factor that drives the market growth of healthcare fraud detection in healthcare market is increase in number of patients seeking health insurance. The other factors such as increase in fraudulent cases, and misuse of funds offered by healthcare boost the growth of the healthcare fraud detection market. A small number of auditors must manually evaluate and pinpoint the dubious medical insurance claims to manually discover healthcare fraud.

However, more effective and automated methods of detecting healthcare frauds have been developed because to recent breakthroughs in machine learning and data mining techniques. In recent years, there has been an increase in interest in mining healthcare data for fraud detection also boosting the global healthcare fraud detection market. The breakthrough advances in machine learning and artificial intelligence, increased in data security concern in healthcare industry restraining the global healthcare fraud detection market.
The healthcare fraud detection market segmented on the basis of type, component, application, end user and region. By type, the market is segmented into descriptive analytics, predictive analytics and prescriptive analysis. By component the market is fragmented into service and software. By application the market divided into insurance claims review and payment integrity.

By end user, the market is segmented into healthcare payer, government agencies and others. The healthcare payer further segmented into public payer and private payer. The others segment includes employers, healthcare providers and third-party service providers. Region wise, the market is analyzed across North America, Europe, Asia-Pacific, and LAMEA.
Major key players that operate in the global healthcare fraud detection market are International Business Machines Corporation (IBM), Optum, Verscend Technologies, McKesson Corporation, FAIR ISAAC Corporation, SAS Institute Inc., HCL Technologies, Wipro Limited, Conduent, CGI Group, DXC Technology Company, UnitedHealth Group, Exlservice Holdings Inc., Scio inspire Corp, LexisNexis, OSP Labs, Northrop Grumman.

Key Benefits For Stakeholders
●This report provides a quantitative analysis of the market segments, current trends, estimations, and dynamics of the healthcare fraud detectionmarket analysis from 2021 to 2031 to identify the prevailing healthcare fraud detection market opportunities.
●The market research is offered along with information related to key drivers, restraints, and opportunities.
●Porter's five forces analysis highlights the potency of buyers and suppliers to enable stakeholders make profit-oriented business decisions and strengthen their supplier-buyer network.
●In-depth analysis of the healthcare fraud detection market segmentation assists to determine the prevailing market opportunities.
●Major countries in each region are mapped according to their revenue contribution to the global market.
●Market player positioning facilitates benchmarking and provides a clear understanding of the present position of the market players.
●The report includes the analysis of the regional as well as global healthcare fraud detection market trends, key players, market segments, application areas, and market growth strategies.

Key Market Segments
By Application
● Insurance Claims Review
● Payment Integrity
By End User
● Healthcare Payer
○ Type
○ Public Payers
○ Private players
● Government Agencies
● Others
By Type
● Descriptive Analytics
● Predictive Analytics
● Prescriptive Analysis
By Component
● Services
● Software

By Region
● North America
○ U.S.
○ Canada
○ Mexico
● Europe
○ Germany
○ France
○ UK
○ Italy
○ Spain
○ Rest of Europe
● Asia-Pacific
○ China
○ Australia
○ India
○ South Korea
○ Rest of Asia-Pacific
○ Japan
● LAMEA
○ Brazil
○ Saudi Arabia
○ South Africa
○ Rest of LAMEA

● Key Market Players
○ International Business Machines Corporation (IBM)
○ Optum Inc.
○ Verscend Technologies
○ McKesson Corporation
○ FAIR ISAAC Corporation
○ SAS Institute Inc.
○ HCL Technologies
○ WIPRO LIMITED
○ Conduent
○ CGI Group
○ DXC Technology Company
○ UnitedHealth Group, Inc.
○ Exlservice Holdings Inc.
○ Cotiviti Inc.
○ LexisNexis
○ OSP Labs
○ Northrop Grumman
○ Northrop Grumman Corp

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ご注文は、お電話またはWEBから承ります。お見積もりの作成もお気軽にご相談ください。

本レポートと同分野（モバイルヘルス、mHealth）の最新刊レポート

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